Zimmer Biomet (Germany)

Health services research faces the challenge of providing sound recommendations for action for the further development of health systems and care. The application of causal inference methods offers health services researchers an excellent opportunity to identify causal relationships under everyday conditions. The role of clinical trials with a classic randomised controlled trial (RCT) design is recognised as suitable for gaining insights that help establish causal inference, but other methodological approaches to generating evidence also play an important role in health services research.The discussion paper presents key concepts and assumptions of causal inference and highlights their relevance for health services research. The paper makes it clear that in order to fulfil the assumptions, it is necessary to integrate theory, contextual knowledge, understanding of mechanisms and formal concepts, such as directed acyclic graphs (DAGs), into a suitable empirical study design. To this end, RCTs, quasi-experimental methods, causal machine learning, target trial emulation, in silico trials and the mixed-methods approach of integrated inference are presented and discussed in terms of their applicability in health services research and their internal and external validity.All of the approaches presented here can contribute to the estimation of causal effects when used in a targeted manner and in accordance with the central assumptions. Their suitability depends largely on the research question, data quality, theoretical modelling and contextual knowledge. The combination of complementary designs and high quality data sources can increase the robustness of causal conclusions.Causal inference in health services research is not only a methodological procedure, but an integrative process that systematically combines theory, methodology and contextual knowledge. By consistently linking these aspects, health services research can generate differentiated and actionable insights that go beyond correlative analyses and enable an understanding of the mechanisms of causal processes. This can lead, for example, to evidence-supported recommendations that critically examine the often non-evidence-based status quo and reliably evaluate the benefits of new models.

Emotion regulation (ER) is a relevant transdiagnostic mechanism for mental health, involving cognitive, behavioral, and physiological processes. Children of parents with a mental illness (COPMI) are at increased risk for ER difficulties, yet the integration of state and trait ER assessments remains underexplored. This study employs a multimodal approach to assess ER in COPMI compared to children of parents without a mental illness (COPWMI). We examined trait ER via self-report in 195 children (96 COPMI/99 COPWMI) aged 4 - 16, cognitive state ER using an emotional Go/Nogo task in 243 children (96 COPMI/147 COPWMI) aged 7 - 16, and physiological state ER through heart rate and electrodermal activity responses during an emotional video task in 124 children (49 COPMI/75 COPWMI) aged 7 - 16. No significant group differences emerged in cognitive or physiological measures of state ER. Emotional stimuli affected physiological responses across all participants, but COPMI did not show heightened reactivity or diminished affective inhibitory control compared to COPWMI. Furthermore, trait ER did not significantly predict state ER responses across groups. These findings challenge the assumption of broad ER impairments in COPMI and highlight the importance of considering situational and environmental factors when assessing ER. The absence of universal state ER deficits suggests that some COPMI may compensate for potential vulnerabilities.

Abstract Background Routinely collected health data are increasingly used to generate real-world evidence for therapeutic decision-making. Yet, stakeholders, including clinicians, pharmaceutical industry representatives, patient advocacy groups, and statisticians, prioritize different aspects of data quality, analysis, and interpretation. Without explicit consideration of these perspectives, analyses risk being fragmented, misaligned with end-user needs, or lacking transparency. Methods We developed a stakeholder-inclusive conceptual framework for modeling routine health data, informed by an interdisciplinary workshop and supported by targeted literature examples. The framework maps stakeholder priorities to methodological requirements and identifies analytical strategies that enable integration of diverse perspectives. Results Clinicians prioritize interpretability and clinical relevance; the pharmaceutical industry emphasizes regulatory compliance and real-world evidence generation; patient groups highlight transparency, inclusion of patient-reported outcomes, and privacy protection; and statisticians focus on bias control and methodological rigor. Our framework illustrates how these priorities can be explicitly incorporated into modeling strategies. Multistate models exemplify a methodological approach that operationalizes these requirements by capturing dynamic disease trajectories, integrating intermediate outcomes, and offering graphical interpretability. Beyond specific methodological choices, clinical research relies fundamentally on statistical expertise. Depending on the research goal, statisticians’ roles can range from providing statistical consultations for standard analyses to applying or adapting advanced methods for more complex analyses to developing new methods for research questions that require novel approaches due to their specific characteristics. Conclusions The stakeholder-inclusive framework provides methodological guidance for designing analyses of routine health data that are clinically meaningful, scientifically rigorous, and socially acceptable. By aligning the research question with the intended perspective from the beginning, it supports more robust and transparent evidence generation, with multistate models serving as a flexible tool to operationalize this integration.

Abstract Purpose Myasthenia gravis (MG) is a rare neuromuscular disease. In-person appointments in specialized centers are not readily available, especially on short notice. The purpose of this study was to analyze patient-specialist communication through a telemedical platform. Methods In a randomized controlled study 45 MG patients were observed over three months. The intervention group (N=30) was monitored via a mobile application (‘app’) that enabled chat function and assessed MG-specific outcome measures and data from external devices. We quantitatively evaluated communication patterns for patients and specialists from the chat. Controls (N=15) received standard of care. Perceived MG specialist accessibility and contact with physicians outside the study team regarding MG-related issues was assessed in both groups. Findings In total, MG specialists were contacted a median of six times per patient via the chat concerning medical (54.5%), technical (25.3%), organizational (19.1%), and other (1.1%) topics. Specialists initiated contact a median of eight times per patient, most frequently addressing medical concerns (49.4%), 38.4% of which were recommendations for medication changes. Specialists also addressed technical (40.0%), organizational (9.4%) and other (1.3%) issues. Contacts with physicians outside the study team did not differ significantly between groups. Perceived specialist accessibility was rated significantly higher with telemedical support by the intervention group. Conclusions MG patients have a significant demand for timely advice especially concerning treatment recommendations. Future studies should evaluate the potential of telemedical solutions to prevent worsening of symptoms. Trial registration The study was registered under DRKS00029907 on August 19, 2022.

Older adults often exhibit insufficient physical activity (PA) and sedentary behavior (SB), which contributes to chronic disease, functional decline, and reduced independence. From a public health perspective, low-threshold interventions are needed to promote PA and reduce SB in older adults. Wearable activity trackers offer a promising strategy by providing real-time feedback and integrating behavior change techniques (BCTs). However, evidence regarding their efficacy in older adults is still limited and inconsistent. A systematic review of randomized controlled trials (RCTs) was conducted to evaluate the efficacy of activity trackers in increasing PA and reducing SB in older adults. This review extended previous research by examining the behavioral intervention components in activity trackers. In addition, it assessed a broad range of PA and SB outcomes, categorizing them according to intervention duration to differentiate between short- (≤ 3 months), medium- (> 3–6 months), and long-term effects (> 6 months). Eligible studies met the following criteria: (a) RCT; (b) older adults (≥ 60 years or mean age ≥ 60 years minus 1 SD) not recruited for specific diseases; (c) use of an activity tracker ≥ 2 weeks; (d) comparison with at least one control group; and (e) report of PA or SB outcomes. Four databases (CENTRAL, PubMed, SPORTDiscus, Web of Science) and additional sources were screened. Risk of bias was assessed using the Cochrane RoB 2 tool. Eighteen RCTs (N = 2,841) met the inclusion criteria. Most studies reported positive effects of activity trackers on PA, particularly regarding steps per day and intensity-related PA outcomes, with effects observed in the short- and medium-term. For SB, some studies indicated a reduction in self-reported SB, whereas no improvements were reported for objectively measured SB. Most interventions combined activity trackers with multiple BCTs, most frequently goal setting, self-monitoring, and feedback on behavior. Interventions using these components more consistently demonstrated positive outcomes, indicating that specific BCTs may facilitate behavior change. Activity trackers can promote PA in older adults, particularly when combined with behavioral strategies. However, their impact on objectively measured SB and long-term outcomes remains unclear. Future interventions should explicitly target SB, align BCTs with SB-related goals, and promote long-term engagement among older adults.

Hunger and satiety are influenced by both the amount and composition of dietary fat. This exploratory analysis examined the effects of meals enriched with different amounts of either canola oil (rich in unsaturated fatty acids) or coconut oil (rich in saturated fatty acids) on postprandial changes in subjective hunger and satiety ratings, related hormones, attention, and memory. Twenty-nine participants with an increased cardiometabolic disease risk (e.g., body mass index, 30.2 ± 2.6 kg/m2) received 4 isoenergetic (∼4200 kJ) test meals in a randomized order: high-fat meals (HFMs) (50 g) or low-fat meals (LFMs) (25 g) containing either canola or coconut oil. Hunger and satiety ratings, blood samples for ghrelin and peptide YY (PYY), and cognitive tests were conducted at fasting and over a 6-h postprandial period. The data were analyzed via linear mixed models. Lower serum ghrelin levels were observed after canola oil meals [canola compared with coconut: -27,700 (confidence interval: -40,700, -14,700) min × pg/mL] with no effect of fat amount on incremental area under the curve data [HFM compared with LFM: -9500 (-22,500, 3500) min × pg/mL]. Serum PYY concentrations were higher after HFMs [HFM compared with LFM: 8600 (2100, 15,200) min × pg/mL]. LFMs resulted in lower subjective hunger ratings compared with HFMs [HFM compared with LFM: 250 (71, 430) min × score-value]. Our data indicate that predominantly the amount of dietary fat, rather than its type, affects hunger and satiety. At the hormonal level, canola oil suppressed postprandial hunger to a greater extent than coconut oil. This trial was registered at ClinicalTrials.gov (https://clinicaltrials.gov/) as NCT05208346.

ObjectiveTo evaluate a multidisciplinary therapy program with a neurological focus for individuals with post-COVID-19 condition, aiming to reduce symptom burden and improve functioning.DesignNon-experimental prospective before–after study.Subjects/patientsIndividuals diagnosed with post-COVID-19 condition, defined as experiencing persistent signs and symptoms for more than 12 weeks after initial SARS-CoV-2 infection.MethodsWe conducted a 2 week multidisciplinary rehabilitation program at the Schoen Clinic Bad Aibling, Germany. The intervention included multi-professional therapies. Assessments were conducted at six time points: baseline at the start of the 2-week control period, pre- and post-intervention, and at 2, 8, and 24 weeks post-intervention. Mixed-effects regression models were used to analyze changes over time. Outcome measures included health-related quality of life (HRQoL; EQ-5D-5L), fatigue, anxiety, depression, symptom severity, breathing difficulties, cognitive function, functional disability, and performance measures.ResultsA total of 47 participants (60% female; mean age 49 years; range 21–80) were enrolled, with a median of 220 days (Q1: 156–Q3: 376) since initial infection. No significant improvement in HRQoL, grip strength, cognitive function, walking capacity, or balance was observed during the intervention compared to the control period. Fatigue, anxiety, depression, symptom severity, functional disability, and dyspnea improved significantly.ConclusionThis study indicates beneficial effects of a 2 week multidisciplinary therapy program on symptom burden and functional outcomes of the post-COVID-19 condition. Further research including randomized controlled trials is warranted.Trial registrationGerman Clinical Trials Register, DRKS00029415. Registered 04 July, 2022. Retrospectively registered. https://drks.de/search/en/trial/DRKS00029415Supplementary InformationThe online version contains supplementary material available at 10.1007/s00415-026-13693-5.

Esophageal cancer remains a leading cause of cancer-related mortality worldwide. Esophagectomy is the cornerstone of curative treatment, but the optimal surgical approach remains debated. Newer techniques such as hybrid esophagectomy, minimally invasive esophagectomy (MIE), and robot-assisted minimally invasive esophagectomy (RAMIE) have been developed to improve perioperative outcomes while maintaining oncologic efficacy. We aim to compare the effects of open, hybrid, minimally invasive, and robot-assisted approaches to esophagectomy on survival and perioperative outcomes in patients with esophageal cancer. A systematic review and network meta-analysis (NMA) were conducted, including 10 reports from 6 randomized controlled trials identified via PubMed, Cochrane Library, Embase, CINAHL, ClinicalTrials.gov, and ICTRP. Comparative analyses between open esophagectomy (OE), hybrid laparoscopy-thoracotomy (HYB LapS-ThoT), MIE, and RAMIE were performed using random-effects NMA models. Hazard ratios (HR), odds ratios (OR), and mean differences (MD) were calculated for outcomes. There were no significant differences in overall survival among OE, HYB LapS-ThoT, MIE, and RAMIE. Pulmonary complications were significantly lower with MIE (OR 0.47, 95% CI 0.33-0.69, p<0.0001) and RAMIE (OR 0.39, 95% CI 0.27-0.57, p<0.0001) compared to OE. RAMIE yielded a higher lymph node harvest (MD 1.56, 95% CI 0.58-2.54, p=0.002) and lower reoperation rates (OR 0.65, 95% CI 0.45-0.93, p=0.020) than OE. HYB LapS-ThoT was associated with increased anastomotic leakage compared to OE (OR 1.66, 95% CI 1.02-2.69, p=0.041). MIE and RAMIE significantly reduce pulmonary complications without compromising survival. Hybrid approaches appear to increase the risk of anastomotic leakage. These findings support minimally invasive techniques, especially RAMIE; however, more evidence and further studies are needed to allow for a clearer and more definitive conclusion.

BackgroundThe recruitment of participants with a high risk of decline is crucial for the success of clinical trials in the earliest phases of Alzheimer´s Disease (AD), where treatment benefits could be the largest. Subjective cognitive decline (SCD) and minor neuropsychological deficits (MNPD) are associated with an increased risk of cognitive decline, making them promising predictors for this risk stratification. However, the prognostic value of their interplay is understudied.MethodWe pooled and analyzed data from cognitively unimpaired participants from the Alzheimer´s Disease Neuroimaging Initiative (N = 599), DZNE Longitudinal Cognitive Impairment and Dementia study (N = 618), and National Alzheimer's Coordinating Center (N = 11,975). SCD was measured using questionnaires or anamnestic data. MNPD was defined as a median neuropsychological test performance of z≤‐0.5. We assessed the association of MNPD and SCD with the conversion to mild cognitive impairment (MCI) and dementia (cox regression) and baseline amyloid and tau positivity – measured by PET/CSF (logistic regression). We adjusted these models for the study cohorts and demographic covariates. Using power analyses, we calculated the sample sizes necessary to detect a 30% reduction in the risk of progressing to MCI over 4.5 years in amyloid positive participants.ResultIn the overall sample (N = 13,192), the SCD‐/MNPD+ (+:present, ‐:absent; HR=3.13[2.68‐3.66]), SCD+/MNPD‐ (HR=1.97[1.76‐2.20]), and SCD+/MNPD+ (HR=6.23[5.23‐7.42]) groups had an increased risk of MCI compared to the SCD‐/MNPD‐ group. These groups also had an increased risk of dementia. In amyloid positive participants (n = 497), this pattern persisted for the progression to MCI, while only the SCD+/MNPD+ group had an increased risk for dementia. In participants with biomarker data (n = 2,616), the SCD+/MNPD‐ (OR=1.47[1.20‐1.81]) and SCD+/MNPD+ (OR=1.64[1.04‐2.59]) groups had an increased risk of amyloid positivity. The risk of tau positivity was increased in the SCD+/MNPD+ group (OR=2.10[1.13‐3.90]). In the power analyses, the required clinical trial size was reduced by approximately one third after excluding SCD‐/MNPD‐ individuals and approximately two thirds by focusing only on SCD+/MNPD+ individuals (Figure).ConclusionSCD and MNPD have a complementary prognostic value. SCD+/MNPD+ individuals are at particularly high risk of pathology and decline. These clinical symptoms should be taken into account in the recruitment for clinical trials in preclinical AD.

ABSTRACTAlthough count data are collected in many experiments, their analysis remains challenging, especially in small sample sizes. Until now, linear or generalized linear models in Poisson or Negative Binomial distributional families have often been used. However, these data frequently show signs of over‐, underdispersion, or even zero‐inflation, casting doubt on these distributional assumptions and leading to inaccurate test results. Since their distributions are usually skewed, data transformations (e.g., log‐transformation) are not unusual. This underscores the need for statistical methods not to hinge on specific distributional assumptions. We delve into multiple contrast tests that allow general contrasts (e.g., many‐to‐one or all‐pairs comparisons) to analyze count data in multi‐arm trials. The methods vary in their effect and variance estimation, as well as in approximating the joint distribution of multiple test statistics, including frequently used methods such as linear and generalized linear models, and data transformations. An extensive simulation study demonstrates that a resampling version effectively controls the Type I error rate in various situations, while also highlighting the method's limitations, including overly liberal Type I error rates. Some standard methods, which have inflated Type I error rates, further underscore the need for alternative approaches. Real data applications further emphasize the applicability of these methods.